1. Field of the Invention
The present invention generally relates to a face annotation techniques, in particular, to a face annotation method and a face annotation system employed within a collaborative framework and a multiple-kernel learning algorithm.
2. Description of Related Art
Due to the rapid growth of portable consumer devices and wireless communications services, people can send and receive multimedia content and information at any time and from any place. Increasing numbers of people are using wireless mobile applications to share personal photos via online social networks (OSNs) such as Facebook and Google+. This has resulted in large quantities of photo collections in OSNs. In order to facilitate effective browsing, searching, categorization, and exportation (e.g. sending or printing) of photo collections, it is important to develop face annotation techniques that allow users to manage and organize personal photos in OSNs, etc.
By using face annotation techniques, users are able to tag facial images in personal photos with the names of individuals. These techniques can be divided into three categories: manual face annotation, semi-automatic face annotation, and fully automatic face annotation. Manual face annotation for large numbers of photo collections is labor-intensive and time-consuming; because of this, many studies have investigated semi-automatic face annotation or fully automatic face annotation techniques. Semi-automatic face annotation requires user interaction and feedback to determine the identity label of each individual query for each personal photo. Unfortunately, these interaction and feedback procedures result in additional time consumption for practical face annotation systems. In order to avoid user intervention and manual operation, fully automatic face annotation systems which have been developed which integrate computer vision-based face detection and face recognition (FR) techniques.
The accuracy of face detection has improved considerably over the past decade, allowing it to consequently become a mature technique. Face recognition (FR) techniques comprise a very active field of research and can be divided into two types: single FR classifiers and multiple FR classifiers. Single FR classifiers do not perform well under uncontrolled, fluctuant conditions which feature changes in illumination, facial expressions, and so on. However, a combination of different single FR classifiers results in improvement of face recognition accuracy even under fluctuant conditions.
Recently, the framework of a distributed FR database and its corresponding FR classifier has been proposed to facilitate a reduction in the computational complexity, and improvement of the accuracy of face annotation, for each member in an OSN. However, these FR database methods that rely on only a single FR classifier may cause unfavourable accuracy results under uncontrolled conditions.
Accordingly, how to efficiently and accurately achieve highly reliable face annotation results has become an essential topic to researchers for the development of face annotation techniques in OSNs.